The development of Artificial Intelligence (AI) modeled human understanding of the real world (RW), as summarized by Dreyfus et al., “Why Computers May Never Think Like People,” Harvard Technology Review, 42 (January 1986), is a collection of facts, rules of operation, and experiences. This collection of symbolic information varies with human skill in a particular application domain, by varying across a skill level scale from novice, through advanced beginner, and on to competent, proficient, and expert abilities. The variation of skill proceeds from the collection of facts to goal based action, with intuition and analysis resulting into the instant understanding of the expert. There is a response time associated with skill. Feigenbaum et al., “Signal-To-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, 23 (Spring 1982) referred to the collection of RW data as a signal detection process. The construction of this data into symbols creates a modeled symbol-structure, composed of low-level attribute elements being clustered and grouped by RW objects (RWO) into an application hierarchy.
This process of construction was called an inference between knowledge sources and observations using an IF-THEN hypothesis-testing algorithm. Inferencing could use pattern invoked, heuristic methods or logical, tree-searched, rule-based methods. Here, the connection between RWO and the modeled understanding is through an atomic object layer (with at least one attribute or feature) and a detection process on the existence of that attribute, with a probability of detection (PD) and a probability of false-alarm detection (PFA). These atomic elements are built into more complex structures of an object diagram (OD). Solinsky, “An Artificial Intelligence Perspective on the Sonar Problem—Recognition Control Strategy in a Relational Data Base,” #T85-1199/301, Rockwell International, Anaheim, Calif. (October 1985) showed that a relational database (RDB) was a means of constructing this knowledge base (KB) as that of clustering detected feature values of modeled RWOs. Here the example RWO is a tuple, and the fields of the RDB are the populated feature values.
An important element of RDB access in this application is shown by Li, A PROLOG Database System, Research Studies Press Ltd., John Wiley, NY, N.Y. (1984) to incorporate predicate calculus. Feature detection is the process of signal detection and feature extraction through algorithmic processing of atomic level sensor measurements using “a priori” knowledge, including a detection threshold setting value (d). Just as in the human skill model, an observation time (T) is required before a detection event can occur, and hence the symbolic construction process is discretized in time. Solinsky, “A Man/Machine Performance Model for Analyzing Sonar System Design”, #T86, Rockwell International, Anaheim, Calif. (December 1986) showed that the information rate of this symbolic construction process is constant, but varies in resolution analysis between sorting Yes/No decisions at low resolution, and analysis at high resolution, and involves feedback over a fixed construction/extraction time using short term and long term memory models (STM, LTM). After symbolic construction, a sequence of atomic events can be used to retain temporal or spatial event clustering (as in object spatial movement or eye-scanning motion).
The false alarm rate (FAR) in feature-detection is the ratio of PFA to T. The process of matching a set of detected object features to a new set of features is that of classification, and Solinsky, “The Use of Expert Systems in Machine Vision Recognition,” VISION'86 Conference, Detroit, Mich., 4-139 (June 1986) showed that the Expert System (ES) paradigm was applicable to this process in vision applications. The feedback of the setting of the threshold detection value “d” is provided by the speed of the clustering processes required. This method of threshold controlled decision process performance is based on one of five methods described later by Klir, Advances in Computers, Vol. 36 edited by M. C. Yovits, Academic Press, NY, N.Y., 255, (1993).
The use of object detection in visual tasks is a human expert skill implemented as a Gestalt process. Solinsky has generalized the approach for a) feature selection (see Solinsky, “A Generalized Feature Extraction Approach,” VISION '87 Conference, Detroit, Mich., 8-57, June 1987), b) classification using neural-networks (see Solinsky, “Machine Vision Tutored Learning Using Artificial Neural Systems Classification,” VISION '88 Conference, Detroit, Mich., 1-MS88-490, June 1988), and c) decision processes for low FAR regimes (see Solinsky, “Evaluating System Performance in Low False Alarm Rate Regimes,” #JCS/ASD/92-001, Advanced System Division, SAIC, La Jolla, Calif., February 1992). Solinsky, “A Method for Compact Information Characterization in a Finite, Discrete Data Set,” #JCS/ASD/93-003, Advanced Systems Division, SAIC, La Jolla, Calif. (Apr. 1993) has also presented generalized information structuring for characterizing a discrete data set in a large dimensional feature space. This technique uses an important analysis method termed a “balloon matrix” for measuring correlation in N-dimensional hyperspaces. The hyperspace “diameter” of the balloon is found in a search time, which is related to the correlation lag and the correlation order. The compact form of a subspace representation uses a fractal dimensional ordering. In this way a much larger, and even infinite space can be made into a compact, set of finite spaces. Solinsky, et al, “Higher-order Statistical Application in Acoustics with Reference to Nonlinearities in Chaos,” Third International Symposium on Signal Processing Applications (HOSSPA 92), Gold Coast, Queensland, Australia (August 1992) has shown higher order statistical (HOS) correlation to exist in acoustic data from mammals and in neural data from bat brain neurons. Solinsky, et. al, “Signal Analysis of Nonlinear Dynamics and Higher-Order Statistics,” SPIE Proceed. 2037 Chaos/Dynamics, San Diego, Calif. 163 (July 1993) has shown this HOS correlation to be present in other data set applications including financial data. This HOS correlation represents the Gestalt process of the N-dimensional hyperspace correlation (for N>2).
The structure for knowledge representation in AI has been dominated by the object model, with linked object diagrams (OD), such as shown by Booch, Object-Oriented Analysis and Design, Benjamin/Cummings Pub. Co., Redwood City, Calif. (1994) with class attribute relationships, Coad and Yourdon, Object-Oriented Design, Prentice Hall Inc., Englewood Cliffs, N.J. (1991) for super/sub object and associated object relationships, and the current modeling using the Universal Modeling Language (UML) by Fowler and Scott, UML Distilled, CRC Press, Addison Wesley Langman, Inc., Menlo Park, Calif. (2000), Rumbaugh, Jacobson and Booch, The UML Reference Manual, Addison-Wesley Langman, Inc., Menlo Park, Calif. (1999), and Booch, Rumbaugh, and Jacobson, The UML User Guide, Addison Wesley Langman, Inc., Menlo Park, Calif. (1999).
The construction of information systems that are intelligent, by being self-erecting, and capable of implementation in a computing machine, was explored by Solinsky, “Intelligent Information Systems,” SAIC White Paper, (February 1995). Here the use of human interaction is used to construct information from data, as defined as “assessment by methods unrelated to the data itself.” Intelligent systems have the ability to acquire and use information. These systems evolve information from data cases using an information “channel” of flow similar to the previously modeled information rate shown earlier by Solinsky (December 1986) to be limited. The process in the intelligent information system using computing machinery involves a set of metrics:                1) The information has a finite lifetime (r) and channel length L, which because of a fixed flow rate, c, limits the ability of the system to achieve instantaneous synchronization. Rather, a unit of synchronicity is finite, and is in terms of a “chron” (crn≡cτ/L).        2) The distribution of information through replication must have a user's transaction cost (CR) to limit resource wasting and time consuming processes.        3) The system must incorporate a feedback process to minimize the average cm unit in the processes ( crn≡{crn}min).        4) The system customizes the user interaction to minimize the user's emotional level as determined through an interface (e.g., Gelemter, “The Muse in the Machine,” Free Press Division of MacMillian, Inc., NY, N.Y. (1994)).        5) Diverse user interaction creates information latency, which incurs a cost (CL) that randomizes information through equivocation (Solinsky (December 1986)).        6) Diverse user interaction also creates information uncertainty as an injection of information randomness or noise (Solinsky (December 1986)).        7) User feedback is used to retain information accuracy by consistent and frequent decision confirmation.        8) Both object-modeled information flow (e.g., OD) and data flow must exist in the system, where the information rate channel capacity (CC) limits the information flow rate, and the computing hardware channel bandwidth (BW) limits the data rate. This process involves a user decision of transforming unknown data into objects for investigation and analysis (e.g., unfamiliar objects are data flow). Known objects are discarded by the user (e.g., familiar objects are information flow).        9) The user actions are modeled by two levels of memory, STM and LTMs with the LTM model of familiar objects being accessed through a set of multileveled CC limited pathways. This provides an implementation of situation awareness for a specific user interaction.        10) The system incorporates an overall cost function (CF) which limits the use of information flow by a redundancy factor (Re for Re=1.0 as no information flow, and Re=0.0 as an infinite flow for a state of confusion). This CF is a nonlinear, direct mapping of Re, and is inversely mapped to the information rate CC. The CF is user and application dependent. The process of transforming data-to-objects is modeled as human perception and transforming objects-to-data is modeled as human instantiation.        
A series of research proposals (Murphy and Solinsky, “Automated Model Correlator and Metamodel Building Environments,” Accord Solutions SBIR Proposal A95-065 (January 1995), “The Information Computer—An Intelligent Systems Component for Consistent Abstraction of Collaborator Experience,” Accord Solutions (1996), and Accord Solutions ATP proposal “Components for a Concurrent Paradigm,” (May 1997)) addressed the application of this information construction structure into a series of decision surfaces based on information content in the analyzed data. A series of briefings on the information system elements (Solinsky and Murphy, “The Information Computer”, Accord Solutions briefing presented from August 1995-February 1996 to McDonnell Douglas Corp. and Cubic Corp.) identified an important concept in intelligent information construction systems, which can be implemented in computing machines. This information computer (IC) consists of:                1) A LTM representation of information as a linked set of objects in an OD, with ordering based on the user's viewpoint, with objects of importance being closest, and objects of less importance being furthest away in “distance” (as a link count) from the most important object to the user.        2) The OD viewpoint allows objects of self-containment to be chunked into a single, macro object, which has links to other objects, but is fully represented in the user's viewpoint context as a single macro object. In this way the entropy H, which is a function of the OD object count (O), link count (L), and average hyperspace spanning vector ({tilde under (D)}) is defined as H≡(O, L, {tilde under (D)}) and is minimized by chunking. In many instances, the chunked objects can capture a subspace of the general OD attribute set.        3) The user decision/response is modeled as one of three outcomes of unfamiliar objects: a) discarded as not of interest, b) modified using STM to be corrected, and c) identified as being new, and entered into LTM. Familiar objects are automatically responded to with minimal time and effort as an automatic response. The use of a forced-choice interaction model with the user is a cognitive science method of creating confusion in the representation to force the user into a reactive and possibly emotional response which accesses the user's actual LTM information.        4) The IC uses an Action Channel (AC) to construct confusion in the representation of the OD confronting the user. The AC is similar to a Shannon communication channel for data flow, except the AC constricts the information flow as discussed earlier through equivocation and uncertainty. A specific element of the AC is an access to the OD stored in LTM, with the combination of new objects randomly included as input to the channel. The channel itself involves a type specified linking process (i.e., a verb) involving a propagation time step, which changes the output OD, by adding uncertainty through additional random links added and removed from the OD as a noise process, and equivocation as an object removal process to be on the order of the input object count in order to retain the same entropy of input-to-output OD space.        5) The AC model for OD modification is based on binary construction to a single object by a link type change as noun/verb/object/labeling for labels of: a) no change (NOP), b) create a new forward link (p addition), c) combine two objects (y combining), and d) create a backward link (b addition). The noun element in the change is the identity of the original object of the OD where the linking process occurs.        
The proposals by Solinsky and Murphy extended the decision process to classification techniques using a neural network decision process and a generalized decision process as represented by Klir, Advances in Computers, Vol. 36 edited by M. C. Yovits, Academic Press, NY, N.Y., 255, (1993). The general decision process is an expansion of PD/PFA decision in a typical likelihood, cost function format, and includes a) classical set theory (Hartley, “Transmission of Information”, The Bell Systems Technical Journal 1, 535-563 (1928)); b) fuzzy set theory (Zadeh, “Fuzzy Sets”, Information and Control 8 (3) 338-353 (1965)); c) probability theory (Shannon, “The Mathematical Theory of Communication,” The Bell Systems Technical Journal, 27, 379-423, 623-656 (1948)); d) possibility theory (Zadeh, “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets and Systems 1 (1), 3-28 (1978)); and e) evidence theory (Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, N.J. (1976) and Demster, “Upper and Lower Probability Inferences Based on a Sample from a Finite Univariate Population,” a) Biometrika 54, 515-528; b) Annals of Mathematical Statistics 38, 325-339 (1967) as bounded probability, and belief functions (Shafer, “Belief Functions and Possibility Measures” in Analysis of Fuzzy Information Vol 1 edited by J. C. Bezedek, CRC Press, Boca Raton, Fla., 51-84 (1985))). Klir summarizes this decision process as being either involving fuzziness or ambiguity, where “strife-ambiguity” is a disagreement of alternatives and “nonspecified-ambiguity” is a set of unspecified alternatives, i.e. issues involved with decisions which require resolution because of disagreement or lack of information, e.g. at an emotional level.
The decision process of the IC was modeled to include Klir's min/max uncertainty ranges of decisions to become invariant to uncertainty through the user decision process, such that the evolved LTM OD is the same information from all user viewpoints which then becomes a common set of information or knowledge.
This background work in AI and information modeling has not included the combinations described for the IC and the use of neural-networks (NN) in decision processes of OD representations. Eliot, “Ruling Neural Networks,” AI Expert, 8 (February 1995) has shown that a NN is not as easy to understand as an AI ES, which involves only rules. This is because ES logic involves discrete binary Yes/No states and NNs involve sigmoid-shaped decision surfaces with a focus on the “Maybe” region in the Yes-to-No transition region of the sigmoid. While ES's attempted to include this as a user input/review process with a certainty factor in decisions, this approach was unsuccessful because its final output was not a complete decision. The lure of the ES rule-based modeling is its compactness, but this can better be represented in a predicate calculus format with NN decisions as incorporated into this invention. The current invention transforms the OD models and AI of the IC to a mathematical hyperspace representation which is efficiently represented and operated on for RW applications using efficient bit-level manipulation and computation.
A series of patents have dealt with ODs and the use of object models in applications, and particularly with RDB accessing. U.S. Pat. No. 3,970,992 deals with a keyboard macro for retrieval application in a data processing system. U.S. Pat. No. 4,906,940 deals with a “rubberized” template matching approach for guiding an object on a road. U.S. Pat. No. 5,506,580 involves a data compression approach using a character stream library encoding. U.S. Pat. No. 5,548,755 is a hashing technique to optimize RDB-grouped query access. U.S. Pat. No. 5,586,218 uses case-based reasoning for information gathering in a RW sensor derived data set. Here, decision and case construction use a Genetic Algorithm decision process. U.S. Pat. No. 5,701,400 constructs a tool-kit for financial advisors based on weighted logic ES, IF-THEN-ELSE rules to data sets in a RDB.
U.S. Pat. No. 5,712,960 incorporates abductive reasoning as a meta interpreter for updating a communication data base management system. U.S. Pat. No. 5,768,586 uses an object structure for data configuration in system modeling of complex enterprises, which begins with high level user descriptions and constructs low level descriptions from the modeled process. U.S. Pat. No. 5,778,378 is a document retrieval application of an object-oriented (OO) framework for word indexing and parsing. U.S. Pat. Nos. 5,790,116; 5,794,001, and 5,900,870 use a GUI to construct a hierarchical definition of an object structure in a data record application. Templates are used for selecting data fields linkable to a collection folder of instantiations. Various 2-D graphics, are used, such as a node-arc graph. U.S. Pat. No. 5,806,075 uses a triggering methodology based on data values, of data duplications between local and remote sites. U.S. Pat. No. 5,832,205 is a memory failure detection process based on comparative instruction analysis. U.S. Pat. No. 5,875,108 is a GUI interface intelligence through adaptive pattern recognition of historic actions as in a button pushing effort using a VCR remote control. U.S. Pat. No. 5,893,106 incorporates a server support to client users, which encapsulates a class hierarchy of 3-D graphics in data base applications. U.S. Pat. No. 5,905,855 corrects errors in computer systems by two state analysis of initial and final state reference points.
U.S. Pat. No. 5,911,581 incorporates a metric for determining mental ability of complex task solving, and models reaction time, awareness thresholds, attention levels, information capacity and LTM access speed. U.S. Pat. No. 5,915,252 uses a consistency check between a data source and target to simplify a user's job in data transferring. It includes protocol free construction of ES object links embodied in a common user interface. U.S. Pat. No. 5,926,832 is a means of increasing memory access by memory address analysis and storage. U.S. Pat. No. 5,936,860 is an application to warehouse control functions by a user with OO data modeling. U.S. Pat. No. 5,953,707 uses a planned decision model in a client/server database from various user viewpoints in sales planning and inventory management as a user GUI without an OO model. U.S. Pat. No. 5,958,061 uses a cache to store states for instruction translation. U.S. Pat. No. 5,966,712 applies to the use of RDB storage of biomolecular sequences which, compares sequence frames and groups of frames and displays results to the user.
U.S. Pat. No. 5,970,482 applies to the application of feed-forward decisions to the data mining process. A predictive model is used to compare and rank symbolic data correlation significance. U.S. Pat. No. 5,978,790 uses an edge-labeled tree approach to match input elements to output restructuring in a semi-structured database. The tree can only be structured in 2-D data base structures. U.S. Pat. No. 5,991,776 involves an application of RDB indexing by linking tuple identifications to the document, but does not use an OO structure.
U.S. Pat. No. 5,995,958 manages a database through the use of acyclic graphs by mapping an infinite data set to a finite storage of a X-function core representation. It is applied to RDBs with user query support, but does not involve OO models, since the links are independent of the node contents. U.S. Pat. No. 5,999,940 incorporates a 2-D visual representation for user discovery and visualization for applications involving multiple access to databases, such as in health and doctor treatment areas. U.S. Pat. No. 6,002,865 uses a multi-dimensional set of spreadsheet pages to construct a database by multiple levels of resolution. U.S. Pat. No. 6,003,024 deals with row selection of 2-D database accesses for attribute-based record selection as an intersection of attribute correlation in 2-D. U.S. Pat. No. 6,006,230 involves a client/server remote user access application modeled in OO technology for proxy mapping.
U.S. Pat. No. 6,009,199 is a decision process for classification in decision trees. It is an iterative mapping of subspace to full space based on discriminative processes. The perceptron model is used in classification.
The previously cited U.S. Pat. Nos. 5,832,205; 5,905,855; 5,926,832; and 5,958,061 and U.S. Pat. No. 6,011,908 deal with the iterative translation of computer microprocessor instructions to a target set of processor states embodied in a chip set with gated memory buffering. The process speeds up the instruction execution as historic references occur, and the system consequently becomes more adept at predicting the next sequential executable instructions. This is an N=2 form of correlation prediction.